Overview

Dataset statistics

Number of variables19
Number of observations2186
Missing cells8045
Missing cells (%)19.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory523.8 B

Variable types

Numeric13
Categorical6

Alerts

ReporterISO has constant value "CHN"Constant
FlowDesc has constant value "Import"Constant
PartnerISO has a high cardinality: 222 distinct valuesHigh cardinality
Key has a high cardinality: 2186 distinct valuesHigh cardinality
Country Code has a high cardinality: 201 distinct valuesHigh cardinality
j has a high cardinality: 188 distinct valuesHigh cardinality
RefYear is highly overall correlated with Period and 1 other fieldsHigh correlation
Period is highly overall correlated with RefYear and 1 other fieldsHigh correlation
Cifvalue is highly overall correlated with PrimaryValue and 5 other fieldsHigh correlation
PrimaryValue is highly overall correlated with Cifvalue and 5 other fieldsHigh correlation
year is highly overall correlated with RefYear and 1 other fieldsHigh correlation
gdp is highly overall correlated with Cifvalue and 6 other fieldsHigh correlation
population is highly overall correlated with Cifvalue and 3 other fieldsHigh correlation
sum_pos_tweets is highly overall correlated with Cifvalue and 5 other fieldsHigh correlation
count_tweets is highly overall correlated with Cifvalue and 6 other fieldsHigh correlation
sum_likes is highly overall correlated with gdp and 3 other fieldsHigh correlation
sum_retweets is highly overall correlated with Cifvalue and 5 other fieldsHigh correlation
Country Code has 202 (9.2%) missing valuesMissing
year has 202 (9.2%) missing valuesMissing
gdp has 202 (9.2%) missing valuesMissing
population has 202 (9.2%) missing valuesMissing
j has 326 (14.9%) missing valuesMissing
dist has 356 (16.3%) missing valuesMissing
sum_pos_tweets has 1311 (60.0%) missing valuesMissing
count_tweets has 1311 (60.0%) missing valuesMissing
sum_political_tweets has 1311 (60.0%) missing valuesMissing
sum_likes has 1311 (60.0%) missing valuesMissing
sum_retweets has 1311 (60.0%) missing valuesMissing
PartnerISO is uniformly distributedUniform
Key is uniformly distributedUniform
Country Code is uniformly distributedUniform
j is uniformly distributedUniform
Key has unique valuesUnique
sum_pos_tweets has 141 (6.5%) zerosZeros
sum_political_tweets has 800 (36.6%) zerosZeros
sum_likes has 197 (9.0%) zerosZeros
sum_retweets has 229 (10.5%) zerosZeros

Reproduction

Analysis started2023-04-11 14:47:47.672790
Analysis finished2023-04-11 14:48:11.143928
Duration23.47 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

RefYear
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.511
Minimum2012
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:11.195812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2017
Q32019
95-th percentile2021
Maximum2021
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8745101
Coefficient of variation (CV)0.001425487
Kurtosis-1.2252865
Mean2016.511
Median Absolute Deviation (MAD)2.5
Skewness-0.0067243127
Sum4408093
Variance8.2628085
MonotonicityIncreasing
2023-04-11T16:48:11.290043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2018 220
10.1%
2019 220
10.1%
2021 220
10.1%
2013 219
10.0%
2016 219
10.0%
2017 219
10.0%
2020 219
10.0%
2012 218
10.0%
2014 217
9.9%
2015 215
9.8%
ValueCountFrequency (%)
2012 218
10.0%
2013 219
10.0%
2014 217
9.9%
2015 215
9.8%
2016 219
10.0%
2017 219
10.0%
2018 220
10.1%
2019 220
10.1%
2020 219
10.0%
2021 220
10.1%
ValueCountFrequency (%)
2021 220
10.1%
2020 219
10.0%
2019 220
10.1%
2018 220
10.1%
2017 219
10.0%
2016 219
10.0%
2015 215
9.8%
2014 217
9.9%
2013 219
10.0%
2012 218
10.0%

Period
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.511
Minimum2012
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:11.387931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2017
Q32019
95-th percentile2021
Maximum2021
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8745101
Coefficient of variation (CV)0.001425487
Kurtosis-1.2252865
Mean2016.511
Median Absolute Deviation (MAD)2.5
Skewness-0.0067243127
Sum4408093
Variance8.2628085
MonotonicityIncreasing
2023-04-11T16:48:11.478699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2018 220
10.1%
2019 220
10.1%
2021 220
10.1%
2013 219
10.0%
2016 219
10.0%
2017 219
10.0%
2020 219
10.0%
2012 218
10.0%
2014 217
9.9%
2015 215
9.8%
ValueCountFrequency (%)
2012 218
10.0%
2013 219
10.0%
2014 217
9.9%
2015 215
9.8%
2016 219
10.0%
2017 219
10.0%
2018 220
10.1%
2019 220
10.1%
2020 219
10.0%
2021 220
10.1%
ValueCountFrequency (%)
2021 220
10.1%
2020 219
10.0%
2019 220
10.1%
2018 220
10.1%
2017 219
10.0%
2016 219
10.0%
2015 215
9.8%
2014 217
9.9%
2013 219
10.0%
2012 218
10.0%

ReporterISO
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.2 KiB
CHN
2186 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6558
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHN
2nd rowCHN
3rd rowCHN
4th rowCHN
5th rowCHN

Common Values

ValueCountFrequency (%)
CHN 2186
100.0%

Length

2023-04-11T16:48:11.576475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T16:48:11.682292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
chn 2186
100.0%

Most occurring characters

ValueCountFrequency (%)
C 2186
33.3%
H 2186
33.3%
N 2186
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6558
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 2186
33.3%
H 2186
33.3%
N 2186
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 6558
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 2186
33.3%
H 2186
33.3%
N 2186
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6558
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 2186
33.3%
H 2186
33.3%
N 2186
33.3%

FlowDesc
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size151.6 KiB
Import
2186 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters13116
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowImport
2nd rowImport
3rd rowImport
4th rowImport
5th rowImport

Common Values

ValueCountFrequency (%)
Import 2186
100.0%

Length

2023-04-11T16:48:11.768524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-11T16:48:11.875074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
import 2186
100.0%

Most occurring characters

ValueCountFrequency (%)
I 2186
16.7%
m 2186
16.7%
p 2186
16.7%
o 2186
16.7%
r 2186
16.7%
t 2186
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10930
83.3%
Uppercase Letter 2186
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 2186
20.0%
p 2186
20.0%
o 2186
20.0%
r 2186
20.0%
t 2186
20.0%
Uppercase Letter
ValueCountFrequency (%)
I 2186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13116
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 2186
16.7%
m 2186
16.7%
p 2186
16.7%
o 2186
16.7%
r 2186
16.7%
t 2186
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 2186
16.7%
m 2186
16.7%
p 2186
16.7%
o 2186
16.7%
r 2186
16.7%
t 2186
16.7%

PartnerISO
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct222
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size145.4 KiB
W00
 
10
CUW
 
10
BES
 
10
NCL
 
10
VUT
 
10
Other values (217)
2136 

Length

Max length7
Median length3
Mean length3.0182983
Min length3

Characters and Unicode

Total characters6598
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW00
2nd rowAFG
3rd rowALB
4th rowDZA
5th rowAND

Common Values

ValueCountFrequency (%)
W00 10
 
0.5%
CUW 10
 
0.5%
BES 10
 
0.5%
NCL 10
 
0.5%
VUT 10
 
0.5%
NZL 10
 
0.5%
NIC 10
 
0.5%
NER 10
 
0.5%
NGA 10
 
0.5%
19,00 F 10
 
0.5%
Other values (212) 2086
95.4%

Length

2023-04-11T16:48:11.969949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w00 10
 
0.5%
bdi 10
 
0.5%
dom 10
 
0.5%
brn 10
 
0.5%
alb 10
 
0.5%
dza 10
 
0.5%
and 10
 
0.5%
ago 10
 
0.5%
atg 10
 
0.5%
aze 10
 
0.5%
Other values (213) 2096
95.4%

Most occurring characters

ValueCountFrequency (%)
A 488
 
7.4%
R 480
 
7.3%
N 426
 
6.5%
M 418
 
6.3%
S 395
 
6.0%
B 359
 
5.4%
L 348
 
5.3%
T 320
 
4.8%
G 320
 
4.8%
C 289
 
4.4%
Other values (25) 2755
41.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6422
97.3%
Decimal Number 136
 
2.1%
Space Separator 20
 
0.3%
Other Punctuation 10
 
0.2%
Connector Punctuation 10
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 488
 
7.6%
R 480
 
7.5%
N 426
 
6.6%
M 418
 
6.5%
S 395
 
6.2%
B 359
 
5.6%
L 348
 
5.4%
T 320
 
5.0%
G 320
 
5.0%
C 289
 
4.5%
Other values (16) 2579
40.2%
Decimal Number
ValueCountFrequency (%)
9 48
35.3%
0 40
29.4%
1 29
21.3%
7 10
 
7.4%
5 9
 
6.6%
Space Separator
ValueCountFrequency (%)
  10
50.0%
10
50.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6422
97.3%
Common 176
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 488
 
7.6%
R 480
 
7.5%
N 426
 
6.6%
M 418
 
6.5%
S 395
 
6.2%
B 359
 
5.6%
L 348
 
5.4%
T 320
 
5.0%
G 320
 
5.0%
C 289
 
4.5%
Other values (16) 2579
40.2%
Common
ValueCountFrequency (%)
9 48
27.3%
0 40
22.7%
1 29
16.5%
  10
 
5.7%
, 10
 
5.7%
7 10
 
5.7%
_ 10
 
5.7%
10
 
5.7%
5 9
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6588
99.8%
None 10
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 488
 
7.4%
R 480
 
7.3%
N 426
 
6.5%
M 418
 
6.3%
S 395
 
6.0%
B 359
 
5.4%
L 348
 
5.3%
T 320
 
4.9%
G 320
 
4.9%
C 289
 
4.4%
Other values (24) 2745
41.7%
None
ValueCountFrequency (%)
  10
100.0%

Cifvalue
Real number (ℝ)

Distinct2184
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8120334 × 1010
Minimum9
Maximum2.6843627 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:12.102039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile8953.5
Q118730681
median2.8651095 × 108
Q33.8242945 × 109
95-th percentile5.302828 × 1010
Maximum2.6843627 × 1012
Range2.6843627 × 1012
Interquartile range (IQR)3.8055638 × 109

Descriptive statistics

Standard deviation1.3730537 × 1011
Coefficient of variation (CV)7.5774194
Kurtosis216.21251
Mean1.8120334 × 1010
Median Absolute Deviation (MAD)2.8648118 × 108
Skewness14.312647
Sum3.9611051 × 1013
Variance1.8852766 × 1022
MonotonicityNot monotonic
2023-04-11T16:48:12.247052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2753 2
 
0.1%
71 2
 
0.1%
1.818199228 × 10121
 
< 0.1%
3421521148 1
 
< 0.1%
21774777 1
 
< 0.1%
33160256 1
 
< 0.1%
40063 1
 
< 0.1%
2172086000 1
 
< 0.1%
81763041 1
 
< 0.1%
2825845569 1
 
< 0.1%
Other values (2174) 2174
99.5%
ValueCountFrequency (%)
9 1
< 0.1%
13 1
< 0.1%
31 1
< 0.1%
33 1
< 0.1%
34 1
< 0.1%
39 1
< 0.1%
45 1
< 0.1%
71 2
0.1%
72 1
< 0.1%
76 1
< 0.1%
ValueCountFrequency (%)
2.684362679 × 10121
< 0.1%
2.133605397 × 10121
< 0.1%
2.079285499 × 10121
< 0.1%
2.069567865 × 10121
< 0.1%
1.959234625 × 10121
< 0.1%
1.949992315 × 10121
< 0.1%
1.843792939 × 10121
< 0.1%
1.818199228 × 10121
< 0.1%
1.679564325 × 10121
< 0.1%
1.587920688 × 10121
< 0.1%

PrimaryValue
Real number (ℝ)

Distinct2184
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8120334 × 1010
Minimum9
Maximum2.6843627 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:12.385787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile8953.5
Q118730681
median2.8651095 × 108
Q33.8242945 × 109
95-th percentile5.302828 × 1010
Maximum2.6843627 × 1012
Range2.6843627 × 1012
Interquartile range (IQR)3.8055638 × 109

Descriptive statistics

Standard deviation1.3730537 × 1011
Coefficient of variation (CV)7.5774194
Kurtosis216.21251
Mean1.8120334 × 1010
Median Absolute Deviation (MAD)2.8648118 × 108
Skewness14.312647
Sum3.9611051 × 1013
Variance1.8852766 × 1022
MonotonicityNot monotonic
2023-04-11T16:48:12.531148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2753 2
 
0.1%
71 2
 
0.1%
1.818199228 × 10121
 
< 0.1%
3421521148 1
 
< 0.1%
21774777 1
 
< 0.1%
33160256 1
 
< 0.1%
40063 1
 
< 0.1%
2172086000 1
 
< 0.1%
81763041 1
 
< 0.1%
2825845569 1
 
< 0.1%
Other values (2174) 2174
99.5%
ValueCountFrequency (%)
9 1
< 0.1%
13 1
< 0.1%
31 1
< 0.1%
33 1
< 0.1%
34 1
< 0.1%
39 1
< 0.1%
45 1
< 0.1%
71 2
0.1%
72 1
< 0.1%
76 1
< 0.1%
ValueCountFrequency (%)
2.684362679 × 10121
< 0.1%
2.133605397 × 10121
< 0.1%
2.079285499 × 10121
< 0.1%
2.069567865 × 10121
< 0.1%
1.959234625 × 10121
< 0.1%
1.949992315 × 10121
< 0.1%
1.843792939 × 10121
< 0.1%
1.818199228 × 10121
< 0.1%
1.679564325 × 10121
< 0.1%
1.587920688 × 10121
< 0.1%

Key
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct2186
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.1 KiB
W00_2012
 
1
PNG_2018
 
1
NOR_2018
 
1
FSM_2018
 
1
MHL_2018
 
1
Other values (2181)
2181 

Length

Max length12
Median length8
Mean length8.0182983
Min length8

Characters and Unicode

Total characters17528
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2186 ?
Unique (%)100.0%

Sample

1st rowW00_2012
2nd rowAFG_2012
3rd rowALB_2012
4th rowDZA_2012
5th rowAND_2012

Common Values

ValueCountFrequency (%)
W00_2012 1
 
< 0.1%
PNG_2018 1
 
< 0.1%
NOR_2018 1
 
< 0.1%
FSM_2018 1
 
< 0.1%
MHL_2018 1
 
< 0.1%
PLW_2018 1
 
< 0.1%
PAK_2018 1
 
< 0.1%
PAN_2018 1
 
< 0.1%
PRY_2018 1
 
< 0.1%
TGO_2018 1
 
< 0.1%
Other values (2176) 2176
99.5%

Length

2023-04-11T16:48:12.658729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19,00 10
 
0.5%
x 10
 
0.5%
w00_2012 1
 
< 0.1%
bih_2012 1
 
< 0.1%
bel_2012 1
 
< 0.1%
cpv_2012 1
 
< 0.1%
brb_2012 1
 
< 0.1%
arm_2012 1
 
< 0.1%
bgd_2012 1
 
< 0.1%
bhr_2012 1
 
< 0.1%
Other values (2178) 2178
98.7%

Most occurring characters

ValueCountFrequency (%)
2 2843
16.2%
0 2445
13.9%
_ 2196
12.5%
1 1996
 
11.4%
A 488
 
2.8%
R 480
 
2.7%
N 426
 
2.4%
M 418
 
2.4%
S 395
 
2.3%
B 359
 
2.0%
Other values (30) 5482
31.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8880
50.7%
Uppercase Letter 6422
36.6%
Connector Punctuation 2196
 
12.5%
Space Separator 20
 
0.1%
Other Punctuation 10
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 488
 
7.6%
R 480
 
7.5%
N 426
 
6.6%
M 418
 
6.5%
S 395
 
6.2%
B 359
 
5.6%
L 348
 
5.4%
G 320
 
5.0%
T 320
 
5.0%
C 289
 
4.5%
Other values (16) 2579
40.2%
Decimal Number
ValueCountFrequency (%)
2 2843
32.0%
0 2445
27.5%
1 1996
22.5%
9 268
 
3.0%
7 229
 
2.6%
5 224
 
2.5%
8 220
 
2.5%
3 219
 
2.5%
6 219
 
2.5%
4 217
 
2.4%
Space Separator
ValueCountFrequency (%)
  10
50.0%
10
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2196
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11106
63.4%
Latin 6422
36.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 488
 
7.6%
R 480
 
7.5%
N 426
 
6.6%
M 418
 
6.5%
S 395
 
6.2%
B 359
 
5.6%
L 348
 
5.4%
G 320
 
5.0%
T 320
 
5.0%
C 289
 
4.5%
Other values (16) 2579
40.2%
Common
ValueCountFrequency (%)
2 2843
25.6%
0 2445
22.0%
_ 2196
19.8%
1 1996
18.0%
9 268
 
2.4%
7 229
 
2.1%
5 224
 
2.0%
8 220
 
2.0%
3 219
 
2.0%
6 219
 
2.0%
Other values (4) 247
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17518
99.9%
None 10
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2843
16.2%
0 2445
14.0%
_ 2196
12.5%
1 1996
 
11.4%
A 488
 
2.8%
R 480
 
2.7%
N 426
 
2.4%
M 418
 
2.4%
S 395
 
2.3%
B 359
 
2.0%
Other values (29) 5472
31.2%
None
ValueCountFrequency (%)
  10
100.0%

Country Code
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct201
Distinct (%)10.1%
Missing202
Missing (%)9.2%
Memory size139.6 KiB
TLS
 
10
NLD
 
10
CUW
 
10
ABW
 
10
NCL
 
10
Other values (196)
1934 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5952
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowAND
5th rowAGO

Common Values

ValueCountFrequency (%)
TLS 10
 
0.5%
NLD 10
 
0.5%
CUW 10
 
0.5%
ABW 10
 
0.5%
NCL 10
 
0.5%
VUT 10
 
0.5%
NZL 10
 
0.5%
NIC 10
 
0.5%
NER 10
 
0.5%
NGA 10
 
0.5%
Other values (191) 1884
86.2%
(Missing) 202
 
9.2%

Length

2023-04-11T16:48:12.769628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tls 10
 
0.5%
aus 10
 
0.5%
ben 10
 
0.5%
bhs 10
 
0.5%
alb 10
 
0.5%
dza 10
 
0.5%
and 10
 
0.5%
ago 10
 
0.5%
atg 10
 
0.5%
aze 10
 
0.5%
Other values (191) 1884
95.0%

Most occurring characters

ValueCountFrequency (%)
A 460
 
7.7%
R 447
 
7.5%
N 413
 
6.9%
M 392
 
6.6%
L 339
 
5.7%
S 335
 
5.6%
B 328
 
5.5%
T 306
 
5.1%
G 299
 
5.0%
C 278
 
4.7%
Other values (16) 2355
39.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5952
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 460
 
7.7%
R 447
 
7.5%
N 413
 
6.9%
M 392
 
6.6%
L 339
 
5.7%
S 335
 
5.6%
B 328
 
5.5%
T 306
 
5.1%
G 299
 
5.0%
C 278
 
4.7%
Other values (16) 2355
39.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5952
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 460
 
7.7%
R 447
 
7.5%
N 413
 
6.9%
M 392
 
6.6%
L 339
 
5.7%
S 335
 
5.6%
B 328
 
5.5%
T 306
 
5.1%
G 299
 
5.0%
C 278
 
4.7%
Other values (16) 2355
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 460
 
7.7%
R 447
 
7.5%
N 413
 
6.9%
M 392
 
6.6%
L 339
 
5.7%
S 335
 
5.6%
B 328
 
5.5%
T 306
 
5.1%
G 299
 
5.0%
C 278
 
4.7%
Other values (16) 2355
39.6%

year
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)0.5%
Missing202
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean2016.4743
Minimum2012
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:12.863886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12014
median2016
Q32019
95-th percentile2021
Maximum2021
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8644524
Coefficient of variation (CV)0.0014205251
Kurtosis-1.220005
Mean2016.4743
Median Absolute Deviation (MAD)2
Skewness0.0094079217
Sum4000685
Variance8.2050878
MonotonicityIncreasing
2023-04-11T16:48:12.953180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2014 201
9.2%
2013 200
9.1%
2015 200
9.1%
2012 199
9.1%
2016 199
9.1%
2017 199
9.1%
2018 199
9.1%
2019 198
9.1%
2020 197
9.0%
2021 192
8.8%
(Missing) 202
9.2%
ValueCountFrequency (%)
2012 199
9.1%
2013 200
9.1%
2014 201
9.2%
2015 200
9.1%
2016 199
9.1%
2017 199
9.1%
2018 199
9.1%
2019 198
9.1%
2020 197
9.0%
2021 192
8.8%
ValueCountFrequency (%)
2021 192
8.8%
2020 197
9.0%
2019 198
9.1%
2018 199
9.1%
2017 199
9.1%
2016 199
9.1%
2015 200
9.1%
2014 201
9.2%
2013 200
9.1%
2012 199
9.1%

gdp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1984
Distinct (%)100.0%
Missing202
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean4.0900515 × 1011
Minimum36811660
Maximum2.3315081 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:13.072428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum36811660
5-th percentile7.8173935 × 108
Q16.8861102 × 109
median3.013949 × 1010
Q32.0627499 × 1011
95-th percentile1.7200859 × 1012
Maximum2.3315081 × 1013
Range2.3315044 × 1013
Interquartile range (IQR)1.9938888 × 1011

Descriptive statistics

Standard deviation1.7353012 × 1012
Coefficient of variation (CV)4.2427367
Kurtosis89.533944
Mean4.0900515 × 1011
Median Absolute Deviation (MAD)2.8467168 × 1010
Skewness8.8836065
Sum8.1146622 × 1014
Variance3.0112701 × 1024
MonotonicityNot monotonic
2023-04-11T16:48:13.214983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.368206225 × 10101
 
< 0.1%
401932300 1
 
< 0.1%
4.369996926 × 10111
 
< 0.1%
4.217392102 × 10111
 
< 0.1%
1.280866053 × 10101
 
< 0.1%
1.302523991 × 10101
 
< 0.1%
2.11953111 × 10111
 
< 0.1%
914736985.4 1
 
< 0.1%
9846922416 1
 
< 0.1%
3202234637 1
 
< 0.1%
Other values (1974) 1974
90.3%
(Missing) 202
 
9.2%
ValueCountFrequency (%)
36811659.53 1
< 0.1%
38617493.72 1
< 0.1%
38759689.92 1
< 0.1%
39345620.21 1
< 0.1%
41629497.47 1
< 0.1%
45217657.88 1
< 0.1%
47818290.5 1
< 0.1%
54223149.11 1
< 0.1%
55054710.62 1
< 0.1%
63100961.54 1
< 0.1%
ValueCountFrequency (%)
2.331508056 × 10131
< 0.1%
2.138097612 × 10131
< 0.1%
2.106047361 × 10131
< 0.1%
2.053305731 × 10131
< 0.1%
1.947733655 × 10131
< 0.1%
1.869511084 × 10131
< 0.1%
1.820602074 × 10131
< 0.1%
1.773406265 × 10131
< 0.1%
1.755068017 × 10131
< 0.1%
1.684319099 × 10131
< 0.1%

population
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1984
Distinct (%)100.0%
Missing202
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean37430837
Minimum10444
Maximum1.41236 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:13.351592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10444
5-th percentile64603.5
Q11265980
median7178445.5
Q326065778
95-th percentile1.262216 × 108
Maximum1.41236 × 109
Range1.4123496 × 109
Interquartile range (IQR)24799798

Descriptive statistics

Standard deviation1.4165282 × 108
Coefficient of variation (CV)3.7843882
Kurtosis75.993728
Mean37430837
Median Absolute Deviation (MAD)6822149.5
Skewness8.4523757
Sum7.426278 × 1010
Variance2.006552 × 1016
MonotonicityNot monotonic
2023-04-11T16:48:13.489766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2405680 1
 
< 0.1%
110929 1
 
< 0.1%
5311916 1
 
< 0.1%
198387623 1
 
< 0.1%
22577058 1
 
< 0.1%
6572233 1
 
< 0.1%
4900600 1
 
< 0.1%
297298 1
 
< 0.1%
271170 1
 
< 0.1%
105962 1
 
< 0.1%
Other values (1974) 1974
90.3%
(Missing) 202
 
9.2%
ValueCountFrequency (%)
10444 1
< 0.1%
10694 1
< 0.1%
10828 1
< 0.1%
10852 1
< 0.1%
10854 1
< 0.1%
10865 1
< 0.1%
10877 1
< 0.1%
10899 1
< 0.1%
10918 1
< 0.1%
10940 1
< 0.1%
ValueCountFrequency (%)
1412360000 1
< 0.1%
1411100000 1
< 0.1%
1407745000 1
< 0.1%
1407563842 1
< 0.1%
1402760000 1
< 0.1%
1396387127 1
< 0.1%
1396215000 1
< 0.1%
1387790000 1
< 0.1%
1383112050 1
< 0.1%
1379860000 1
< 0.1%

j
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct188
Distinct (%)10.1%
Missing326
Missing (%)14.9%
Memory size136.2 KiB
NLD
 
10
NCL
 
10
VUT
 
10
NZL
 
10
NIC
 
10
Other values (183)
1810 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5580
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowAND
5th rowAGO

Common Values

ValueCountFrequency (%)
NLD 10
 
0.5%
NCL 10
 
0.5%
VUT 10
 
0.5%
NZL 10
 
0.5%
NIC 10
 
0.5%
NER 10
 
0.5%
NGA 10
 
0.5%
NOR 10
 
0.5%
FSM 10
 
0.5%
MHL 10
 
0.5%
Other values (178) 1760
80.5%
(Missing) 326
 
14.9%

Length

2023-04-11T16:48:13.621984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nld 10
 
0.5%
bra 10
 
0.5%
brn 10
 
0.5%
slb 10
 
0.5%
aut 10
 
0.5%
alb 10
 
0.5%
dza 10
 
0.5%
and 10
 
0.5%
ago 10
 
0.5%
atg 10
 
0.5%
Other values (178) 1760
94.6%

Most occurring characters

ValueCountFrequency (%)
A 440
 
7.9%
R 437
 
7.8%
N 393
 
7.0%
M 382
 
6.8%
B 308
 
5.5%
G 299
 
5.4%
L 299
 
5.4%
T 296
 
5.3%
S 287
 
5.1%
C 248
 
4.4%
Other values (16) 2191
39.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5580
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 440
 
7.9%
R 437
 
7.8%
N 393
 
7.0%
M 382
 
6.8%
B 308
 
5.5%
G 299
 
5.4%
L 299
 
5.4%
T 296
 
5.3%
S 287
 
5.1%
C 248
 
4.4%
Other values (16) 2191
39.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 5580
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 440
 
7.9%
R 437
 
7.8%
N 393
 
7.0%
M 382
 
6.8%
B 308
 
5.5%
G 299
 
5.4%
L 299
 
5.4%
T 296
 
5.3%
S 287
 
5.1%
C 248
 
4.4%
Other values (16) 2191
39.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 440
 
7.9%
R 437
 
7.8%
N 393
 
7.0%
M 382
 
6.8%
B 308
 
5.5%
G 299
 
5.4%
L 299
 
5.4%
T 296
 
5.3%
S 287
 
5.1%
C 248
 
4.4%
Other values (16) 2191
39.3%

dist
Real number (ℝ)

Distinct185
Distinct (%)10.1%
Missing356
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean9059.3686
Minimum955.6511
Maximum19297.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:13.745925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum955.6511
5-th percentile3036.238
Q16523.571
median8390.566
Q311903.59
95-th percentile14866.92
Maximum19297.47
Range18341.819
Interquartile range (IQR)5380.019

Descriptive statistics

Standard deviation3856.9611
Coefficient of variation (CV)0.42574282
Kurtosis-0.29654095
Mean9059.3686
Median Absolute Deviation (MAD)2650.464
Skewness0.27927191
Sum16578645
Variance14876149
MonotonicityNot monotonic
2023-04-11T16:48:13.883234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6164.891 10
 
0.5%
14155.35 10
 
0.5%
8648.526 10
 
0.5%
8369.766 10
 
0.5%
11041.03 10
 
0.5%
13785.57 10
 
0.5%
11024.17 10
 
0.5%
11466.06 10
 
0.5%
7031.006 10
 
0.5%
5548.78 10
 
0.5%
Other values (175) 1730
79.1%
(Missing) 356
 
16.3%
ValueCountFrequency (%)
955.6511 10
0.5%
1172.047 10
0.5%
1976.249 10
0.5%
1982.745 10
0.5%
2098.111 10
0.5%
2330.799 10
0.5%
2778.652 10
0.5%
2812.561 10
0.5%
2850.319 10
0.5%
3036.238 10
0.5%
ValueCountFrequency (%)
19297.47 10
0.5%
19175.59 10
0.5%
19079.88 10
0.5%
18311.35 10
0.5%
17614.3 10
0.5%
17389.85 10
0.5%
16666.29 10
0.5%
15364.41 10
0.5%
14937.48 10
0.5%
14866.92 10
0.5%

sum_pos_tweets
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct200
Distinct (%)22.9%
Missing1311
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean110.75429
Minimum0
Maximum4639
Zeros141
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:14.028399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q334
95-th percentile595.9
Maximum4639
Range4639
Interquartile range (IQR)33

Descriptive statistics

Standard deviation386.02809
Coefficient of variation (CV)3.485446
Kurtosis53.807417
Mean110.75429
Median Absolute Deviation (MAD)5
Skewness6.5799895
Sum96910
Variance149017.68
MonotonicityNot monotonic
2023-04-11T16:48:14.165451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 141
 
6.5%
1 117
 
5.4%
2 61
 
2.8%
3 56
 
2.6%
4 40
 
1.8%
5 27
 
1.2%
6 22
 
1.0%
7 15
 
0.7%
14 14
 
0.6%
8 14
 
0.6%
Other values (190) 368
 
16.8%
(Missing) 1311
60.0%
ValueCountFrequency (%)
0 141
6.5%
1 117
5.4%
2 61
2.8%
3 56
 
2.6%
4 40
 
1.8%
5 27
 
1.2%
6 22
 
1.0%
7 15
 
0.7%
8 14
 
0.6%
9 10
 
0.5%
ValueCountFrequency (%)
4639 1
< 0.1%
3953 1
< 0.1%
3729 1
< 0.1%
3216 1
< 0.1%
2522 1
< 0.1%
2330 1
< 0.1%
2316 1
< 0.1%
2141 1
< 0.1%
2063 1
< 0.1%
2041 1
< 0.1%

count_tweets
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct280
Distinct (%)32.0%
Missing1311
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean265.83657
Minimum1
Maximum9943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:14.691678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median16
Q397.5
95-th percentile1281
Maximum9943
Range9942
Interquartile range (IQR)94.5

Descriptive statistics

Standard deviation891.00303
Coefficient of variation (CV)3.3516947
Kurtosis45.444824
Mean265.83657
Median Absolute Deviation (MAD)15
Skewness6.1454845
Sum232607
Variance793886.4
MonotonicityNot monotonic
2023-04-11T16:48:14.839963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 98
 
4.5%
2 72
 
3.3%
3 64
 
2.9%
5 35
 
1.6%
4 29
 
1.3%
12 22
 
1.0%
6 22
 
1.0%
10 18
 
0.8%
8 17
 
0.8%
11 15
 
0.7%
Other values (270) 483
 
22.1%
(Missing) 1311
60.0%
ValueCountFrequency (%)
1 98
4.5%
2 72
3.3%
3 64
2.9%
4 29
 
1.3%
5 35
 
1.6%
6 22
 
1.0%
7 11
 
0.5%
8 17
 
0.8%
9 10
 
0.5%
10 18
 
0.8%
ValueCountFrequency (%)
9943 1
< 0.1%
8541 1
< 0.1%
7967 1
< 0.1%
7440 1
< 0.1%
6004 1
< 0.1%
5668 1
< 0.1%
5609 1
< 0.1%
5570 1
< 0.1%
5373 1
< 0.1%
4949 1
< 0.1%

sum_political_tweets
Real number (ℝ)

MISSING  ZEROS 

Distinct28
Distinct (%)3.2%
Missing1311
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean11.804571
Minimum0
Maximum2836
Zeros800
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:14.959781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum2836
Range2836
Interquartile range (IQR)0

Descriptive statistics

Standard deviation150.04681
Coefficient of variation (CV)12.710907
Kurtosis266.7716
Mean11.804571
Median Absolute Deviation (MAD)0
Skewness15.91247
Sum10329
Variance22514.045
MonotonicityNot monotonic
2023-04-11T16:48:15.061071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 800
36.6%
1 20
 
0.9%
2 10
 
0.5%
3 7
 
0.3%
5 5
 
0.2%
6 4
 
0.2%
12 3
 
0.1%
4 3
 
0.1%
13 2
 
0.1%
20 2
 
0.1%
Other values (18) 19
 
0.9%
(Missing) 1311
60.0%
ValueCountFrequency (%)
0 800
36.6%
1 20
 
0.9%
2 10
 
0.5%
3 7
 
0.3%
4 3
 
0.1%
5 5
 
0.2%
6 4
 
0.2%
7 2
 
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2836 1
< 0.1%
2480 1
< 0.1%
2058 1
< 0.1%
653 1
< 0.1%
644 1
< 0.1%
515 1
< 0.1%
475 1
< 0.1%
153 1
< 0.1%
67 1
< 0.1%
65 1
< 0.1%

sum_likes
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct319
Distinct (%)36.5%
Missing1311
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean1770.5646
Minimum0
Maximum290050
Zeros197
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:15.188388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q3154.5
95-th percentile3998.3
Maximum290050
Range290050
Interquartile range (IQR)153.5

Descriptive statistics

Standard deviation13523.049
Coefficient of variation (CV)7.6377044
Kurtosis283.99065
Mean1770.5646
Median Absolute Deviation (MAD)11
Skewness15.502566
Sum1549244
Variance1.8287285 × 108
MonotonicityNot monotonic
2023-04-11T16:48:15.321460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 197
 
9.0%
1 74
 
3.4%
2 38
 
1.7%
3 34
 
1.6%
4 21
 
1.0%
5 20
 
0.9%
6 15
 
0.7%
11 10
 
0.5%
8 10
 
0.5%
9 9
 
0.4%
Other values (309) 447
 
20.4%
(Missing) 1311
60.0%
ValueCountFrequency (%)
0 197
9.0%
1 74
 
3.4%
2 38
 
1.7%
3 34
 
1.6%
4 21
 
1.0%
5 20
 
0.9%
6 15
 
0.7%
7 8
 
0.4%
8 10
 
0.5%
9 9
 
0.4%
ValueCountFrequency (%)
290050 1
< 0.1%
173770 1
< 0.1%
150127 1
< 0.1%
78382 1
< 0.1%
56259 1
< 0.1%
56251 1
< 0.1%
50321 1
< 0.1%
47142 1
< 0.1%
31330 1
< 0.1%
27562 1
< 0.1%

sum_retweets
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct280
Distinct (%)32.0%
Missing1311
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean595.57257
Minimum0
Maximum67353
Zeros229
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size34.2 KiB
2023-04-11T16:48:15.465356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q3105
95-th percentile1790.2
Maximum67353
Range67353
Interquartile range (IQR)105

Descriptive statistics

Standard deviation3378.1066
Coefficient of variation (CV)5.6720318
Kurtosis211.22583
Mean595.57257
Median Absolute Deviation (MAD)7
Skewness12.826374
Sum521126
Variance11411604
MonotonicityNot monotonic
2023-04-11T16:48:15.601391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 229
 
10.5%
1 80
 
3.7%
2 43
 
2.0%
3 24
 
1.1%
6 21
 
1.0%
5 19
 
0.9%
4 17
 
0.8%
13 14
 
0.6%
9 13
 
0.6%
7 11
 
0.5%
Other values (270) 404
 
18.5%
(Missing) 1311
60.0%
ValueCountFrequency (%)
0 229
10.5%
1 80
 
3.7%
2 43
 
2.0%
3 24
 
1.1%
4 17
 
0.8%
5 19
 
0.9%
6 21
 
1.0%
7 11
 
0.5%
8 10
 
0.5%
9 13
 
0.6%
ValueCountFrequency (%)
67353 1
< 0.1%
41928 1
< 0.1%
32288 1
< 0.1%
18792 1
< 0.1%
17523 1
< 0.1%
14947 1
< 0.1%
13862 1
< 0.1%
13776 1
< 0.1%
13319 1
< 0.1%
13019 1
< 0.1%

Interactions

2023-04-11T16:48:08.635235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:48.191530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:49.751198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:51.279117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:52.776095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:54.272522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:57.069145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:58.569874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:00.134001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:01.723072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:03.397200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:05.335634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:06.968522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:08.774697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:48.314884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:49.861565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:51.393894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:52.889201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:54.373096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:57.177427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:58.685236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:00.249987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:01.854160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:03.533964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:05.470465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:07.106748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:08.915849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:48.420576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:49.965594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:51.506701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:53.000758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:54.471294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:57.285383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:58.801672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:00.366266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:01.986310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:03.667416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:05.606107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:07.243785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:09.036265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:48.533383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:50.080225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:51.621114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:53.117050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:54.572708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:57.392935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:58.918983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:00.480572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:02.101286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:03.784741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:05.721616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:07.362379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:09.158419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:48.648462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:50.194723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:51.736720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:53.233054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:54.675547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:57.502790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:59.036360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:00.599692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:02.217327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:03.901636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:05.840484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:07.483598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:09.285136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:48.730846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:50.277452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:51.819893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:53.315508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:54.749719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:57.584794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:59.126585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:00.691735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:02.336176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:04.023306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:05.963026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:07.607635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:09.422542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:48.841753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:50.388355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:51.927287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:53.423186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:54.850904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:57.690047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:59.241886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:00.810590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:02.464908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:04.153391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:06.095893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:07.741170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:09.557160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:48.963680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:50.511416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:52.051289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:53.547340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:56.324054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:57.812337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:59.370134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:00.941247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:02.598706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:04.283315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:06.227257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:07.873853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:09.687063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:49.087340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:50.630761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:52.174937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:53.670297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:56.440113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:57.934035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:59.499748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:01.065617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:02.745487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:04.407300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:06.362907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:08.010204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:09.806172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:49.230117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:50.753216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:52.288156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:53.785314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:56.558844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:58.055023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:59.620442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:01.202474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:02.871850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:04.824734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:06.477220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:08.128309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:09.931160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:49.357197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:50.878913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:52.407692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:53.902881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:56.684413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:58.178663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:59.743884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:01.319637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:03.001891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:04.965011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:06.598183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:08.251786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:10.054667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:49.482255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:51.006400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:52.524766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:54.019998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:56.808308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:58.305463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:59.869251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:01.456625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:03.127230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:05.084854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:06.715013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:08.378461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:10.184500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:49.614157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:51.137636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:52.647942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:54.144163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:56.937348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:58.435259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:47:59.998130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:01.592089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:03.258448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:05.207618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:06.840228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-11T16:48:08.503080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-11T16:48:15.729096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
RefYearPeriodCifvaluePrimaryValueyeargdppopulationdistsum_pos_tweetscount_tweetssum_political_tweetssum_likessum_retweets
RefYear1.0001.0000.0270.0271.0000.0220.0160.0020.2640.2120.0190.4390.296
Period1.0001.0000.0270.0271.0000.0220.0160.0020.2640.2120.0190.4390.296
Cifvalue0.0270.0271.0001.0000.0350.8700.691-0.2800.5170.5530.3270.4750.522
PrimaryValue0.0270.0271.0001.0000.0350.8700.691-0.2800.5170.5530.3270.4750.522
year1.0001.0000.0350.0351.0000.0220.0160.0020.2640.2120.0190.4390.296
gdp0.0220.0220.8700.8700.0221.0000.769-0.2500.6320.6770.3880.5520.619
population0.0160.0160.6910.6910.0160.7691.000-0.1530.4990.5190.3710.4470.494
dist0.0020.002-0.280-0.2800.002-0.250-0.1531.000-0.293-0.302-0.089-0.286-0.301
sum_pos_tweets0.2640.2640.5170.5170.2640.6320.499-0.2931.0000.9620.3830.8830.897
count_tweets0.2120.2120.5530.5530.2120.6770.519-0.3020.9621.0000.3880.8780.902
sum_political_tweets0.0190.0190.3270.3270.0190.3880.371-0.0890.3830.3881.0000.3620.390
sum_likes0.4390.4390.4750.4750.4390.5520.447-0.2860.8830.8780.3621.0000.942
sum_retweets0.2960.2960.5220.5220.2960.6190.494-0.3010.8970.9020.3900.9421.000

Missing values

2023-04-11T16:48:10.399060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-11T16:48:10.720205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-11T16:48:10.965944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

RefYearPeriodReporterISOFlowDescPartnerISOCifvaluePrimaryValueKeyCountry Codeyeargdppopulationjdistsum_pos_tweetscount_tweetssum_political_tweetssum_likessum_retweets
020122012CHNImportW001.818199e+121818199227571W00_2012NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
120122012CHNImportAFG5.186565e+065186565AFG_2012AFG20122.020357e+1030466479.0AFG4180.438NaNNaNNaNNaNNaN
220122012CHNImportALB1.427209e+08142720886ALB_2012ALB20121.231983e+102900401.0ALB7686.079NaNNaNNaNNaNNaN
320122012CHNImportDZA2.311906e+092311905609DZA_2012DZA20122.090590e+1137260563.0DZA9117.676NaNNaNNaNNaNNaN
420122012CHNImportAND3.240020e+05324002AND_2012AND20123.188809e+0971013.0AND8764.593NaNNaNNaNNaNNaN
520122012CHNImportAGO3.356190e+1033561896917AGO_2012AGO20121.249982e+1125188292.0AGO11769.510NaNNaNNaNNaNNaN
620122012CHNImportATG7.135100e+0471351ATG_2012ATG20121.199948e+0987674.0ATG13681.690NaNNaNNaNNaNNaN
720122012CHNImportAZE2.141617e+08214161731AZE_2012AZE20126.968394e+109295784.0AZE5520.214NaNNaNNaNNaNNaN
820122012CHNImportARG6.560806e+096560805532ARG_2012ARG20125.459824e+1141733271.0ARG19297.470NaNNaNNaNNaNNaN
920122012CHNImportAUS8.456821e+1084568208584AUS_2012AUS20121.546892e+1222733465.0AUS8956.436NaNNaNNaNNaNNaN
RefYearPeriodReporterISOFlowDescPartnerISOCifvaluePrimaryValueKeyCountry Codeyeargdppopulationjdistsum_pos_tweetscount_tweetssum_political_tweetssum_likessum_retweets
217620212021CHNImportUSA1.809719e+11180971932243USA_2021USA20212.331508e+13331893745.0USA10993.680NaNNaNNaNNaNNaN
217720212021CHNImportBFA1.884526e+08188452598BFA_2021BFA20211.973762e+1022100683.0BFA11404.37012.022.00.021.05.0
217820212021CHNImportURY3.623471e+093623470751URY_2021URY20215.931948e+103426260.0URY19175.5901.06.00.02.00.0
217920212021CHNImportUZB1.540988e+091540987879UZB_2021UZB20216.923890e+1034915100.0UZB3943.621NaNNaNNaNNaNNaN
218020212021CHNImportVEN9.977931e+08997793138VEN_2021NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
218120212021CHNImportWLF1.475400e+0414754WLF_2021NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
218220212021CHNImportWSM6.432650e+05643265WSM_2021WSM20218.438424e+08218764.0WSM8268.319NaNNaNNaNNaNNaN
218320212021CHNImportYEM4.708126e+08470812557YEM_2021NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
218420212021CHNImportZMB4.385251e+094385251435ZMB_2021ZMB20212.214763e+1019473125.0ZMB10960.7901.03.00.012.05.0
218520212021CHNImport_X2.060227e+092060227074_X _2021NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN